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78ed45281dd746a265fff16ff75a02e5-Paper-Conference.pdf
Unfortunately, these theoretical results cannot well explain the empirical successes of deep learning well, as they require the model size tobenolargerthan O(n)(thegeneralization boundsbecomevacuousotherwise).
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How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
Devansh Arpit, Víctor Campos, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang
Besides, we observe that existing works still cannotdirectlypropagate the gradient information from deep layers to shallow layers.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.94)
661c1c090ff5831a647202397c61d73c-Paper.pdf
We take a step towards extending this result to deep ResNets. We start by two motivating examples.
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Residual Flows for Invertible Generative Modeling
Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
Neural Information Processing Systems http://nips.cc/
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